Skip to main content

Advertisement

Springer Nature Link
Log in

Soft Cooperative Spectrum Sensing using Quantization Method in the Presence of Smart PUE Attack

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

To overcome the frequency spectrum scarcity, cognitive radio (CR) network is proposed where secondary users opportunistically use vacant frequency band. CR intrinsic properties impose threats to these wireless networks. Smart primary user emulation attack (PUEA) is one of these threats where a malicious user performs spectrum sensing and after being aware of radio environment sends fake signals identical to the primary user signals with the desired signal occurrences in both vacant and occupied licensed frequency bands. In this paper, while considering smart PUEA, we propose a soft cooperative spectrum sensing method. In this method, the sensing information values of N secondary users are transmitted to the fusion center (FC) and combined with some coefficients with the aim of minimizing the error probability of primary user detection, under the given constant false alarm probability. We also propose a method for quantization of this sensing information that should be sent to FC. Furthermore, we have worked on the investigation of channel estimation error impact on detection performance. Simulation results show that our method significantly improves the spectrum sensing probability of the error, in comparison with hard combination scheme, AND fusion and OR fusion rules, either considering quantization or in the presence of channel estimation error.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  1. Alahmadi A, Abdelhakim M, Ren J, Li T (2014) Defense against primary user emulation attacks in cognitive radio networks using advanced encryption standard. IEEE Trans Inf Forens Secur 9(5):772–781

    Article  Google Scholar 

  2. Cabric D, Mishra SM, Brodersen RW (2004) Implementation issues in spectrum sensing for cognitive radios. In: Conference record of the thirty-eighth Asilomar conference on signals, systems and computers, vol 1. IEEE, pp 772–776

  3. Chen C, Cheng H, Yao YD (2011) Cooperative spectrum sensing in cognitive radio networks in the presence of the primary user emulation attack. IEEE Trans Wire Commun 10(7):2135–2141

    Article  Google Scholar 

  4. Chen R, Park JM (2006) Ensuring trustworthy spectrum sensing in cognitive radio networks. In: SDR’06.1 st IEEE Workshop on networking technologies for software defined radio networks. IEEE, pp 110–119

  5. Chen R, Park JM, Hou YT, Reed JH (2008) Toward secure distributed spectrum sensing in cognitive radio networks. IEEE Commun Mag 46(4):50–55

    Article  Google Scholar 

  6. Digham FF, Alouini MS, Simon MK (2007) On the energy detection of unknown signals over fading channels. IEEE Trans Commun 55(1):21–24

    Article  Google Scholar 

  7. Fragkiadakis AG, Tragos EZ, Askoxylakis IG (2013) A survey on security threats and detection techniques in cognitive radio networks. IEEE Commun Surveys Tutor 15(1):428–445

    Article  Google Scholar 

  8. Ganesan G, Li Y (2007) Cooperative spectrum sensing in cognitive radio, part i: two user networks. IEEE Trans Wireless Commun 6(6):2204–2213

    Article  Google Scholar 

  9. Ganesan G, Li Y (2007) Cooperative spectrum sensing in cognitive radio, part ii: multiuser networks. IEEE Trans Wireless Commun 6(6):2214–2222

    Article  Google Scholar 

  10. Ghasemi A, Sousa ES (2005) Collaborative spectrum sensing for opportunistic access in fading environments. In: First IEEE international symposium on new frontiers in dynamic spectrum access networks, 2005. DySPAN 2005. IEEE, pp 131–136

  11. Haghighat M, Sadough SMS (2014) Cooperative spectrum sensing for cognitive radio networks in the presence of smart malicious users. AEU-Int J Electron Commun 68(6):520–527

    Article  Google Scholar 

  12. Haykin S (2005) Cognitive radio: brain-empowered wireless communications. IEEE J Selected Areas Commun 23(2):201–220

    Article  Google Scholar 

  13. Kay SM (1998) Fundamentals of statistical signal processing: detection theory, vol 2

  14. Ma J, Zhao G, Li Y (2008) Soft combination and detection for cooperative spectrum sensing in cognitive radio networks. IEEE Trans Wireless Commun 7(11):4502–4507

    Article  Google Scholar 

  15. Mishra SM, Sahai A, Brodersen RW (2006) Cooperative sensing among cognitive radios. In: 2006 IEEE International conference on communications, vol 4. IEEE, pp 1658–1663

  16. Papoulis A, Pillai SU (2002) Probability, random variables, and stochastic processes. Tata McGraw-Hill Education

  17. Proakis JG, Salehi M, Zhou N, Li X (1994) Communication systems engineering, vol 94. Prentice Hall New Jersey

  18. Qin Z, Li Q, Hsieh G (2013) Defending against cooperative attacks in cooperative spectrum sensing. IEEE Trans Wireless Commun 12(6):2680–2687

    Article  Google Scholar 

  19. Rawat AS, Anand P, Chen H, Varshney PK (2011) Collaborative spectrum sensing in the presence of byzantine attacks in cognitive radio networks. IEEE Trans Signal Process 59(2):774– 786

    Article  MathSciNet  Google Scholar 

  20. Shrivastava S, Rajesh A, Bora PK (2014) Sliding window dixon’s tests for malicious users’ suppression in a cooperative spectrum sensing system. IET Commun 8(7):1065–1071

    Article  Google Scholar 

  21. Urkowitz H (1967) Energy detection of unknown deterministic signals. Proc IEEE 55(4):523–531

    Article  Google Scholar 

  22. Yadav S, Nene MJ (2013) Rss based detection and expulsion of malicious users from cooperative sensing in cognitive radios. In: 2013 IEEE 3rd International of advance computing conference (IACC). IEEE, pp 181–184

Download references

Author information

Authors and Affiliations

  1. Iran University of Science and Technology, Tehran, Iran

    Mohammad Emami & Bahman Abolhassani

  2. Iran Telecommunication Research Center (ITRC), Tehran, 14155-3961, Iran

    Mohammad Reza Jabbarpour & Houman Zarrabi

  3. Department of Computer Engineering, Chung-Ang University, Seoul, 156-756, Republic of Korea

    Jason J. Jung

Authors
  1. Mohammad Emami
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Mohammad Reza Jabbarpour
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Bahman Abolhassani
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Jason J. Jung
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Houman Zarrabi
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Mohammad Emami.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Emami, M., Jabbarpour, M.R., Abolhassani, B. et al. Soft Cooperative Spectrum Sensing using Quantization Method in the Presence of Smart PUE Attack. Mobile Netw Appl 22, 650–659 (2017). https://doi.org/10.1007/s11036-016-0802-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-016-0802-9

Keywords